def test_write_variable_fill_value_is_maintained(self):
if vm.size != 4:
raise SkipTest('vm.size != 4')
dist = OcgDist()
dim = dist.create_dimension('dim', 8, dist=True)
dist.update_dimension_bounds()
var = Variable(name='var', dimensions=dim, fill_value=2.)
var.v()[0] = 1
var.v()[1] = 2
var.get_mask(create=True, check_value=True)
if vm.rank == 0:
path = self.get_temporary_file_path('foo.nc')
else:
path = None
path = vm.bcast(path)
var.parent.write(path)
# if vm.rank == 0:
# self.ncdump(path, header_only=False)
with vm.scoped('read test', [0]):
if not vm.is_null:
invar = RequestDataset(path).create_field()['var']
self.assertEqual(invar.get_mask().sum(), 4)
self.assertEqual(invar.fill_value, 2.)
def test_reorder(self):
x = np.linspace(1, 360, num=7, dtype=float)
x = Variable('lon', value=x, dimensions='dimx')
y = Variable('lat', value=[-40, -20, 0, 20, 40], dimensions='dimy')
t = Variable('time', value=[1, 2, 3], dimensions='dimt')
data = Variable(name='data',
value=np.zeros((3, 5, 7)),
dimensions=['dimt', 'dimy', 'dimx'],
dtype=float)
data.get_value()[:] = 10.
data.get_value()[:, :, 3:x.shape[0]] = 20.
data_mask = data.get_mask(create=True)
data_mask[:, :, 3:x.shape[0]] = 1
data.set_mask(data_mask)
parent = Field(variables=[x, y, t, data])
grid = Grid(parent['lon'],
parent['lat'],
crs=Spherical(),
parent=parent)
desired_y = grid.y.get_value().copy()
grid.wrap()
self.assertFalse(np.any(data[:, :, 0:3].get_value() == 20.))
self.assertFalse(np.any(data.get_mask()[:, :, 0:3]))
grid.reorder()
actual = grid.x.get_value().tolist()
desired = [
-179.5, -119.66666666666666, -59.833333333333314, 0.0, 1.0,
60.833333333333336, 120.66666666666667
]
self.assertEqual(actual, desired)
self.assertNumpyAll(desired_y, grid.y.get_value())
rdata = grid.parent['data']
self.assertTrue(np.all(rdata[:, :, 0:3].get_value() == 20.))
self.assertTrue(np.all(rdata.get_mask()[:, :, 0:4]))
self.assertFalse(np.any(rdata.get_mask()[:, :, 4:]))
self.assertNumpyMayShareMemory(rdata.get_value(), data.get_value())
def test_system_masking(self):
"""Test how the mask is handled by the NetCDF python library."""
var = Variable(name='foo', value=[1, 2, 3], dimensions='three')
self.assertIsNone(var.get_mask())
path = self.get_temporary_file_path('foo.nc')
var.parent.write(path)
invar = Field.read(path)['foo']
self.assertIsNone(invar.get_mask())
def test_write_variable(self):
path = self.get_temporary_file_path('foo.nc')
var = Variable(name='height', value=10.0, dimensions=[])
var.write(path)
rd = RequestDataset(path)
varin = SourcedVariable(name='height', request_dataset=rd)
self.assertEqual(varin.get_value(), var.get_value())
# Test mask persists after write.
v = Variable(name='the_mask', value=[1, 2, 3, 4], mask=[False, True, True, False], dimensions='ephemeral',
fill_value=222)
path = self.get_temporary_file_path('foo.nc')
v.write(path)
rd = RequestDataset(path, driver=DriverNetcdf)
sv = SourcedVariable(name='the_mask', request_dataset=rd)
self.assertEqual(sv.get_value().tolist(), [1, 222, 222, 4])
self.assertNumpyAll(sv.get_mask(), v.get_mask())
def test_system_masking(self):
"""Test behavior of the grid mask. This is an independently managed variable."""
x = Variable('xc', value=[1, 2, 3], dimensions='dimx')
y = Variable('yc', value=[10, 20, 30, 40], dimensions='dimy')
grid = Grid(x, y)
data = Variable('data',
value=np.zeros(grid.shape),
dimensions=['dimy', 'dimx'])
grid.parent.add_variable(data)
gmask = grid.get_mask()
self.assertIsNone(gmask)
self.assertIsNone(grid.mask_variable)
new_mask = np.zeros(grid.shape, dtype=bool)
new_mask[1, 1] = True
grid.set_mask(new_mask, cascade=True)
self.assertIsInstance(grid.mask_variable, Variable)
actual = grid.get_mask()
self.assertNumpyAll(actual, new_mask)
actual = get_variable_names(grid.get_member_variables())
desired = [x.name, y.name, grid._mask_name]
self.assertAsSetEqual(actual, desired)
self.assertNumpyAll(grid.get_mask(), data.get_mask())
path = self.get_temporary_file_path('foo.nc')
grid.parent.write(path)
with self.nc_scope(path) as ds:
actual = ds.variables[grid.mask_variable.name]
self.assertNumpyAll(grid.get_mask(), actual[:].mask)
# Test mask is used when read from file.
actual_field = RequestDataset(path).get()
self.assertNumpyAll(grid.get_mask(), actual_field.grid.get_mask())
self.assertEqual(actual_field.grid.get_mask().sum(), 1)
self.assertTrue(actual_field.grid.is_vectorized)
self.assertEqual(actual_field.grid.get_mask().dtype, bool)
actual_field.set_abstraction_geom()
self.assertNumpyAll(actual_field.geom.get_mask(), grid.get_mask())
def test_write_variable(self):
path = self.get_temporary_file_path('foo.nc')
var = Variable(name='height', value=10.0, dimensions=[])
var.write(path)
rd = RequestDataset(path)
varin = SourcedVariable(name='height', request_dataset=rd)
self.assertEqual(varin.get_value(), var.get_value())
# Test mask persists after write.
v = Variable(name='the_mask',
value=[1, 2, 3, 4],
mask=[False, True, True, False],
dimensions='ephemeral',
fill_value=222)
path = self.get_temporary_file_path('foo.nc')
v.write(path)
rd = RequestDataset(path, driver=DriverNetcdf)
sv = SourcedVariable(name='the_mask', request_dataset=rd)
self.assertEqual(sv.get_value().tolist(), [1, 222, 222, 4])
self.assertNumpyAll(sv.get_mask(), v.get_mask())
